An Introduction to Neural Networks by Kevin GurneyEnglish | Aug. 7, 1997 | ISBN: 1857285034 | 317 Pages | PDF | 4 MBThough mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps.

Bayesian Learning for Neural Networks (Lecture Notes in Statistics) by Radford M. NealEnglish | Aug. 9, 1996 | ISBN: 0387947248 | 195 Pages | PDF | 2 MBArtificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods.